Digital Commons Network^™

Instantaneous Frequency Estimation Of Fm Signals Under Gaussian And Symmetric Alpha-Stable Noise: Deep Learning Versus Time-Frequency Analysis, Huda Saleem Razzaq, Zahir M. Hussain

Research outputs 2022 to 2026

Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation …

A National Survey Of Ability Grouping Practices In Secondary School Physical Education In England, Shaun D. Wilkinson, Dawn Penney

Research outputs 2022 to 2026

This research sought to generate large-scale yet sophisticated data relating to ability grouping practices in physical education (PE) in secondary schools in England, with the intent of extending knowledge of the various ability grouping practices being adopted within and across schools. The prevalence of particular ability grouping practices, processes associated with their application, and factors influencing their use and non-use were explored. Data were collected via a web-based survey of all (3197 at the time of study) mainstream state-funded secondary schools in England. A total of 903 responses were received, giving a response rate of 28.2%. The findings reveal that …

Driving Training-Based Optimization (Dtbo) For Global Maximum Power Point Tracking For A Photovoltaic System Under Partial Shading Condition, Haroon Rehman, Injila Sajid, Adil Sarwar, Mohd Tariq, Farhad I. Bakhsh, Shafiq Ahmad, Haitham A. Mahmoud, Asma Aziz

Research outputs 2022 to 2026

The presence of bypass diodes in photovoltaic (PV) arrays can mitigate the negative effects of partial shading conditions (PSCs), which can cause multiple peak characteristics at the output. However, conventional maximum power point tracking (MPPT) methods can develop errors and detect the local maximum power point (LMPP) instead of the global maximum power point (GMPP) under certain circumstances. To address this issue, several artificial intelligence (AI)-based methods have been proposed, but they result in complicated and unreliable methodologies. This study introduces the driving training-based optimization (DTBO) method, which aims to address the partial shading (PS) problem quickly and reliably in …

Economic Evaluation Of The Very Early Rehabilitation In Speech (Verse) Intervention, J. Kim, G. Sookram, Erin Godecke, Emily Louise Brogan, Elizabeth Armstrong, F. Ellery, T. Rai, M. L. Rose, Natalie Ciccone, S. Middleton, A. Holland, G. J. Hankey, J. Bernhardt, D. A. Cadilhac

Research outputs 2022 to 2026

Introduction: There is limited evidence on the costs and outcomes of patients with aphasia after stroke. The aim of this study was to estimate costs in patients with aphasia after stroke according to the aphasia therapies provided.

Methods: A three-arm, prospective, randomized, parallel group, open-label, blinded endpoint assessment trial conducted in Australia and New Zealand. Usual ward-based care (Usual Care) was compared to additional usual ward-based therapy (Usual Care Plus) and a prescribed and structured aphasia therapy program in addition to Usual Care (the VERSE intervention). Information about healthcare utilization and productivity were collected to estimate costs in Australian dollars …

Seagrass Posidonia Escarpments Support High Diversity And Biomass Of Rocky Reef Fishes, Oscar Serrano Gras, Karina Inostroza, Glenn Hyndes, Alan M. Friedlander, Eduard Serrano, Caitlin Rae, Enric Ballesteros

Research outputs 2022 to 2026

Although seagrass meadows form a relatively homogenous habitat, escarpments, which form three-dimensional structures and originate from the erosion of seagrass peat, can provide important habitat for reef fishes. Here, we compare fish assemblages and habitat structural complexity among seagrass Posidonia australis escarpments and canopies, as well as limestone reef habitats, to understand the role of seagrass escarpments as reef fish habitat in Shark Bay, Western Australia. The total number of fish species, fish biomass, and top predator biomass were significantly higher in seagrass escarpments and reef habitats than in seagrass canopies due to lower habitat structural complexity and thus becoming …

Voltammetric Determination Of Inorganic Arsenic In Mildly Acidified (Ph 4.7) Groundwaters From Mexico And India, Martijn Eikelboom, Yaxuan Wang, Gemma Portlock, Arthur Gourain, Joseph Gardner, Jay Bullen, Paul Lewtas, Matthieu Carriere, Alexandra Alvarez, Arun Kumar, Shane O'Prey, Tamás Tölgyes, Dario Omanović, Subhamoy Bhowmick, Dominik Salaun, Pascal Salaun

Research outputs 2022 to 2026

Routine monitoring of inorganic arsenic in groundwater using sensitive, reliable, easy-to-use and affordable analytical methods is integral to identifying sources, and delivering appropriate remediation solutions, to the widespread global issue of arsenic pollution. Voltammetry has many advantages over other analytical techniques, but the low electroactivity of arsenic(V) requires the use of either reducing agents or relatively strong acidic conditions, which both complicate the analytical procedures, and require more complex material handling by skilled operators. Here, we present the voltammetric determination of total inorganic arsenic in conditions of near-neutral pH using a new commercially available 25 m diameter gold microwire (called …

Invisible Women: Gender Representation In High School Science Courses Across Australia, Kathryn Ross, Shanika Galaudage, Tegan Clark, Nataliea Lowson, Andrew Battisti, Helen Adam, Alexandra K. Ross, Nici Sweaney

Research outputs 2022 to 2026

The visibility of female role models in science is vital for engaging and retaining women in scientific fields. In this study, we analyse four senior secondary science courses delivered across the states and territories in Australia: Biology, Chemistry, Environmental Science, and Physics. We compared male and female representation within the science courses by examining the mentions of male and female scientists along with the context of their inclusions in the syllabuses. We find a clear gender bias with only one unique mention of a female scientist. We also find a clear Eurocentric focus and narrow representation of scientists. This bias …

Improving Accuracy And Efficiency In Seagrass Detection Using State-Of-The-Art Ai Techniques, Md Kislu Noman, Syed M. S. Islam, Jumana Abu-Khalaf, Seyed M. J. Jalali, Paul Lavery

Research outputs 2022 to 2026

Seagrasses provide a wide range of ecosystem services in coastal marine environments. Despite their ecological and economic importance, these species are declining because of human impact. This decline has driven the need for monitoring and mapping to estimate the overall health and dynamics of seagrasses in coastal environments, often based on underwater images. However, seagrass detection from underwater digital images is not a trivial task; it requires taxonomic expertise and is time-consuming and expensive. Recently automatic approaches based on deep learning have revolutionised object detection performance in many computer vision applications, and there has been interest in applying this to …

Going Beyond: Cyber Security Curriculum In Western Australian Primary And Secondary Schools. Final Report, Nicola F. Johnson, Ahmed Ibrahim, Leslie Sikos, Marnie Mckee

Research outputs 2022 to 2026

There is no doubt cyber security is of national interest given the rife nature of cyber crime and the alarming increase of victims who have endured identify theft, fraud and scams. Curriculum within K-12 schools tends to be fixed and any modifications are subject to extensive consultation within a prolonged review cycle. Therefore, this report has gone beyond curriculum to explore the potential of national awareness campaigns and dynamic digital cyber security licences as alternative possibilities for instigation. The role of leaders in various school sectors and systems is critical for a successful roll out. This final report culminates from …

Improved Yolov5-Based Real-Time Road Pavement Damage Detection In Road Infrastructure Management, Abdullah A. Sami, Saadman Sakib, Kaushik Deb, Iqbal H. Sarker

Research outputs 2022 to 2026

Deep learning has enabled a straightforward, convenient method of road pavement infrastructure management that facilitates a secure, cost-effective, and efficient transportation network. Manual road pavement inspection is time-consuming and dangerous, making timely road repair difficult. This research showcases You Only Look Once version 5 (YOLOv5), the most commonly employed object detection model trained on the latest benchmark Road Damage Dataset, Road Damage Detection 2022 (RDD 2022). The RDD 2022 dataset includes four common types of road pavement damage, namely vertical cracks, horizontal cracks, alligator cracks, and potholes. This paper presents an improved deep neural network model based on YOLOv5 for …

Blue Carbon Opportunities: Seagrass Carbon Storage And Accumulation Rates At Johor And Penang, Malaysia, Paul Lavery, Anna Lafratta, Dhivya Nair, Louisa Ponnampalam, Jol Ern Ng, Fairul Izmal Jamal Hisne, Oscar Serrano, Pere Masque´

Research outputs 2022 to 2026

Report prepared as a contribution to the IKI Project “Conservation of biodiversity, seagrass ecosystems and their services – safeguarding food security and resilience in vulnerable coastal communities in a changing climate.

The IKI Seagrass Ecosystem Services Project is a partnership between the CMS, Edith Cowan University, Project Seagrass, Seagrass Watch, Murdoch University, MRS, Blue Ventures, SAN, C3, ZSL, MareCet and Yapeka. The collaboration enhances the understanding of seagrass ecosystem services and the capacity to develop and deliver science-based policy solutions in seagrass conservation. It brings together scientists, policy experts, business development experts and conservation NGOs across the globe to provide …

Reading Group As Method For Feminist Environmental Humanities, James Gardiner, Hayley Singer, Jennifer Hamilton, Astrida Neimanis, Mindy Blaise

Research outputs 2022 to 2026

This article argues that reading groups are a collective field building and research method in Feminist Environmental Humanities, an interdisciplinary scholarly area at the intersections of feminist social justice and environmental concerns. We begin by historicising three Australian Feminist Environmental reading groups (COMPOSTING Feminisms, Eco Feminist Fridays, The Ediths) within a longer feminist tradition, then demonstrate how they respond to declining research funding in the neoliberal university and accelerating ecological crisis. Drawing on survey data, we first thematically code and analyse the results to categorise the groups’ functions and impacts. Departing from more traditional data analysis, we then develop a …

Blue Carbon Opportunities: Seagrass Carbon Storage And Accumulation Rates At Roxas, Palawan, The Philippines, Paul Lavery, Anna Lafratta, Jessa Marie Caabay, Reynante Ramilo, Helbert G. Garay, Maricar Daquioag, Oscar Serrano, Pere Masque´

Research outputs 2022 to 2026

Report prepared as a contribution to the IKI Project “Conservation of biodiversity, seagrass ecosystems and their services – safeguarding food security and resilience in vulnerable coastal communities in a changing climate”

The IKI Project is a partnership between the CMS, Edith Cowan University, Project Seagrass, Seagrass Watch, Murdoch University, MRS, Blue Ventures, SAN, C3, ZSL, MareCet and Yapeka. The collaboration enhances the understanding of seagrass ecosystem services and the capacity to develop and deliver science-based policy solutions in seagrass conservation. It brings together scientists, policy experts, business development experts and conservation NGOs across the globe to provide expert and independent …

Data From An International Multi-Centre Study Of Statistics And Mathematics Anxieties And Related Variables In University Students (The Smarvus Dataset), Jenny Terry, Robert M. Ross, Tamás Nagy, Mauricio Salgado, Patricia Garrido-Vásquez, Jacob O. Sarfo, Susan Cooper, Anke C. Buttner, Tiago J. S. Lima, İbrahim Öztürk, Nazli Akay, Flavia H. Santos, Christina Artemenko, Lee T. Copping, Mahmoud M. Elsherif, Ilija Milovanović, Robert A. Cribbie, Marina G. Drushlyak, Katherine Swainston, Yiyun Shou, Juan D. Leongómez, Nicola Palena, Fitri A. Abidin, Maria F. Reyes-Rodríguez, Yunfeng He, Juneman Abraham, Argiro Vatakis, Kristin Jankowsky, Stephanie N. L. Schmidt, Elise Grimm, Desirée González, Philipp Schmid, Roberto A. Ferreira, Dmitri Rozgonjuk, Neslihan Özhan, Patrick A. O'Connor, Andras N. Zsido, Gregor Stiglic, Darren Rhodes, Cristina Rodríguez, Ivan Ropovik, Violeta Enea, Ratri Nurwanti, Alejandro J. Estudillo, Nataly Beribisky, Karel K. Himawan, Linda M. Geven, Anne H. Van Hoogmoed, Amélie Bret, Jodie E. Chapman, Udi Alter, Zoe M. Flack, Donncha Hanna, Mojtaba Soltanlou, Gabriel Banik, Matúš Adamkovič, Sanne H. G. Van Der Ven, Jochen A. Mosbacher, Hilal H. Şen, Joel R. Anderson, Michael Batashvili, Kristel De Groot, Matthew O. Parker, Mai Helmy, Mariia M. Ostroha, Katie A. Gilligan-Lee, Felix O. Egara, Martin J. Barwood, Karuna Thomas, Grace Mcmahon, Siobhán M. Griffin, Hans-Christoph Nuerk, Alyssa Counsell, Oliver Lindemann, Dirk Van Rooy, Theresa E. Wege, Joanna E. Lewis, Balazs Aczel, Conal Monaghan, Ali H. Al-Hoorie, Julia F. Huber, Saadet Yapan, Mauricio E. Garrido Vásquez, Antonino Callea, Tolga Ergiyen, James M. Clay, Gaetan Mertens, Feyza Topçu, Merve G. Tutlu, Karin Täht, Kristel Mikkor, Letizia Caso, Alexander Karner, Maxine M. C. Storm, Gabriella Daroczy, Rizqy A. Zein, Andrea Greco, Erin M. Buchanan, Katharina Schmid, Thomas E. Hunt, Jonas De Keersmaecker, Peter E. Branney, Jordan Randell, Oliver J. Clark, Crystal N. Steltenpohl, Bhasker Malu, Burcu Tekeş, Tamilselvan Ramis, Stefan Agrigoroaei, Nicholas A. Badcock, Kareena Mcaloney-Kocaman, Olena V. Semenikhina, Erich W. Graf, Charlie Lea, Kalu T. U. Ogba, Fergus M. Guppy, Amy C. Warhurst, Shane Lindsay, Ahmed A. Khateeb, Frank Scharnowski, Leontien De Kwaadsteniet, Kathryn B. Francis, Mariah Lecompte, Lisa A. D. Webster, Kinga Morsanyi, Suzanna E. Forwood, Elizabeth R. Walters, Linda K. Tip, Jordan R. Wagge, Ho Y. Lai, Deborah S. Crossland, Kohinoor M. Darda, Tessa R. Flack, Zoe Leviston, Matthew Brolly, Samuel P. Hills, Elizabeth Collins, Andrew J. Roberts, Wing-Yee Cheung, Sophie Leonard, Bruno Verschuere, Samantha K. Stanley, Iro Xenidou-Dervou, Omid Ghasemi, Timothy Liew, Daniel Ansari, Johnrev Guilaran, Samuel G. Penny, Julia Bahnmueller, Christopher J. Hand, Unita W. Rahajeng, Dar Peterburg, Zsofia K. Takacs, Michael J. Platow, Andy P. Field

Research outputs 2022 to 2026

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts.

Flaws In The Methodologies For Organic Carbon Analysis In Seagrass Blue Carbon Soils, Oscar Serrano, Ines Mazarrasa, James W. Fourqurean, Eduard Serrano, Jeffrey Baldock, Jonathan Sanderman

Research outputs 2022 to 2026

The ability to accurately measure organic carbon (OC) in marine sediments or soils is overall taken for granted in scientific communities, yet this seemingly mundane task remains a methodological challenge when the soil matrix contains calcium carbonate (CaCO3), creating inaccuracies in Blue Carbon estimates. Here, we compared five common methods combining acidification, combustion, and wet oxidation pre-treatments for determination of OC in sediments and soils containing CaCO3 based on the analyses of artificial soil mixtures made of different OC and CaCO3 contents, and multiple soils from Australian seagrass cores. The results obtained showed that methods involving acidification pre-treatment entailed −17 …

Development Of A Scoring Tool For Australian Rural Food Retail Environments, Tracy L. Schumacher, Carissa A. Alderton, Leanne J. Brown, Susan Heaney, Laura Alston, Katherine Kent, Stephanie L. Godrich

Research outputs 2022 to 2026

Current tools scoring the healthiness of food retail outlets do not reflect outlets found in rural locations. This study aimed to adapt pre-existing Australian scoring tools to represent non-metropolitan areas. Rural nutrition experts were identified, and a modified Delphi technique was used to adapt two pre-existing, food-scoring tools in five iterative stages. Stages included identifying all relevant outlets, providing a description and score for each, ensuring consistency between outlet scores and pre-existing, metro-centric tools, and providing instructions for correct use. Six rural nutrition experts were identified and engaged in the modified Delphi technique. The final tool consisted of 12 categories …

Cooperative Deep $Q$ -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In This Article, We Address Two Key Challenges in Deep Reinforcement Learning (DRL) Setting, Sample Inefficiency and Slow Learning, with a Dual-Neural Network (NN)-Driven Learning Approach. in the Proposed Approach, We Use Two Deep NNs with Independent Initialization to Robustly Approximate the Action-Value Function in the Presence of Image Inputs. in Particular, We Develop a Temporal Difference (TD) Error-Driven Learning (EDL) Approach, Where We Introduce a Set of Linear Transformations of the TD Error to Directly Update the Parameters of Each Layer in the Deep NN. We Demonstrate Theoretically that the Cost Minimized by the EDL Regime is an Approximation …

A Resilience-Oriented Multi-Stage Adaptive Distribution System Planning Considering Multiple Extreme Weather Events, Siyuan Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Climate Change May Increase the Risk of an Area Being Hit by Multiple Extreme Weather Events, Which Brings Significant Challenges for Distribution System Planners in an Increasing Renewable Penetration Era. There is an Urgent Need for Planning Approaches to Be More Flexible and Allow for Adaptive Adjustments in the Future to Hedge Against High Uncertainties in Extreme Weather Event Scenarios. in This Work, We Propose a Resilience-Oriented Distribution System Planning Approach that Considers Multiple Extreme Weather Events. a Multi-Stage Hybrid-Stochastic-And-Robust Formulation is Developed to Model Decisions Not Only for Initial Investments, But Also for Adaptive Investments and Emergent Operations in …

Extended Kalman Filter Based Resilient Formation Tracking Control Of Multiple Unmanned Vehicles Via Game-Theoretical Reinforcement Learning, Lei Xue, Bei Ma, Jian Liu, Chaoxu Mu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

In This Paper, We Discuss the Resilient Formation Tracking Control Problem of Multiple Unmanned Vehicles (MUV). a Dynamic Leader-Follower Distributed Control Structure is Utilized to Optimize the Performance of the Formation Tracking. for the Follower of the MUV, the Leader is a Cooperative Unmanned Vehicle, and the Target of Formation Tracking is a Non-Cooperative Unmanned Vehicle with a Nonlinear Trajectory. Therefore, an Extended Kalman Filter (EKF) Observer is Designed to Estimate the State of the Target. Then the Leader of the MUV is Adjusted Dynamically According to the State of the Target. in Order to Describe the Interactions between the …

A Throughput Fast Measurement Method For Two-Antenna Equipped Wireless Mimo Terminals, Penghui Shen, Quan Yu, Daryl G. Beetner, Yihong Qi

Electrical and Computer Engineering Faculty Research & Creative Works

According to the Third Generation Partnership Project Specification, a Period of 8-12.8 H is Required to Evaluate the Multiple-Input-Multiple-Output (MIMO) Performance of a Wireless Terminal for a Single Frequency Point and Channel Model Combination. the Following Article Proposes a Semi-Simulation, Semi-Measurement-Based MIMO throughput Modeling Scheme Which Can Reduce the 8-12.8-H Measurement Time to 40-60 Min, Corresponding to More Than a Ten Times Improvement of the Test Efficiency, Without Loss of the Test Accuracy.

Embeddable Soil Moisture Content Sensor Based On Open–End Microwave Coaxial Cable Resonator, Jing Guo, Yan Tang, Yongji Wu, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

In This Paper, We Propose and Demonstrate a Novel Corrosion-Resistant, Embeddable Open-End Coaxial Cable Soil Moisture Sensor. This Microwave Resonator is Constructed using Two Reflectors Along the Coaxial Line. the First Reflector is a Metal Post at the Signal Input End, Short-Circuiting the Inner Conductor to the Outer Conductor. the Second Reflector Comprises a Welded Metal Plate Parallel to the Open-End of the Coaxial Line, Maintaining a Fixed Gap. a Moisture-Sensitive Polyvinyl Alcohol (PVA) Film is Inserted into This Gap. the Resonance Frequency of the Open-End Coaxial Cable Resonator is Highly Dependent on the Fringe Capacitance, Which Varies with Soil …

Predicting Dissolution Kinetics Of Tricalcium Silicate Using Deep Learning And Analytical Models, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

The dissolution kinetics of Portland cement is a critical factor in controlling the hydration reaction and improving the performance of concrete. Tricalcium silicate (C₃S), the primary phase in Portland cement, is known to have complex dissolution mechanisms that involve multiple reactions and changes to particle surfaces. As a result, current analytical models are unable to accurately predict the dissolution kinetics of C₃S in various solvents when it is undersaturated with respect to the solvent. This paper employs the deep forest (DF) model to predict the dissolution rate of C₃S in the undersaturated solvent. The …

Predicting Compressive Strength And Hydration Products Of Calcium Aluminate Cement Using Data-Driven Approach, Sai Akshay Ponduru, Taihao Han, Jie Huang, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li₂CO₃ content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders' compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of …

Synthesis, Densification, And Cation Inversion In High Entropy (Co,Cu,Mg,Ni,Zn)Al2o4 Spinel, Cole A. Corlett, Nina Obradovic, Jeremy Lee Watts, Eric W. Bohannan, William Fahrenholtz

Materials Science and Engineering Faculty Research & Creative Works

The synthesis, densification behavior, and crystallographic site occupancy were investigated for four different spinel-based ceramics, including a high-entropy spinel (Co_0.2Cu_0.2Mg_0.2Ni_0.2 Zn_0.2)Al₂O₄. Each composition was reacted to form a single phase, but analysis of X-ray diffraction patterns revealed differences in cation site occupancy with the high-entropy spinel being nearly fully normal. Densification behavior was investigated and showed that fully dense ceramics could be produced by hot pressing at temperatures as low as 1375°C for all compositions. Vickers' hardness values were at least 10 GPa for all compositions. The …

The Critical Role Of Happiness In Education: Resilience, Retention, And What Happy Teachers Do Differently, Jill Marie Hayes White

Dissertations and Theses @ UNI

Teachers are unhappy, stressed, and leaving the field at an alarming rate. This paper analyzes how teacher happiness is related to resilience and retention. It also looks for patterns that identify what happy teachers do differently at work. To complete this investigation, current K-12 public school teachers throughout Iowa completed electronic surveys that assessed their happiness, resilience, and work life. These surveys included the Subjective Happiness Scale (Lyubomirsky & Lepper, 1999), the Brief Resilience Scale (Smith, et al., 2008), and 14 additional survey questions created by the researcher. Correlational, comparative, and descriptive analyses were completed to examine potential relationships and …

Real-Time Air Gap And Thickness Measurement Of Continuous Caster Mold Flux By Extrinsic Fabry-Perot Interferometer, Abhishek Prakash Hungund, Hanok Tekle, Bohong Zhang, Ronald J. O'Malley, Jeffrey D. Smith, Rex E. Gerald, Jie Huang

Materials Science and Engineering Faculty Research & Creative Works

Mold Flux plays a critical role in continuous casting of steel. Along with many other functions, the mold flux in the gap between the solidifying steel shell and the mold serves as a medium for controlling heat transfer and as a barrier to prevent shell sticking to the mold. This manuscript introduces a novel method of monitoring the structural features of a mold flux film in real-time in a simulated mold gap. A 3-part stainless-steel mold was designed with a 2 mm, 4 mm and, 6 mm step profile to contain mold flux films of varying thickness. An Extrinsic Fabry-Perot …

A Graduate Recital In Piano, Heather Gillis

Dissertations and Theses @ UNI

Heather Gillis presented a full graduate piano recital on Friday, March 10, 2023. The recital was performed at 8:00 p.m. in Davis Hall in the Gallagher-Bluedorn Performing Arts Center at the University of Northern Iowa. This recital was given in partial fulfillment of the Master of Music degree in Piano Performance and Pedagogy. The program consisted of works by Sergei Prokofiev, Robert Schumann, and Joseph Haydn. This abstract contains further discussion of performed works.

Mapping For Impact: Actionable Spatial Literacy Through Counter-Mapping, Rachel Hansen

Dissertations and Theses @ UNI

Counter-mapping is a technique that young people can use to take informed action on community issues through the mapping process. This mixed-methods study examined how ninth-graders in a large urban district in Iowa developed spatial thinking skills while engaging in counter-mapping in their community and the extent to which they used those maps to take informed action. A nine-week learning progression for counter-mapping scaffolded progress variables across three different spatial thinking standards and one inquiry standard. Findings indicated that students improved throughout the learning progression, but some needed teacher support to conduct spatial inquiries. Chances of reaching the upper anchor …

Managing Reserve Deliverability Risk Of Integrated Electricity-Heat Systems In Day-Ahead Market: A Distributionally Robust Joint Chance Constrained Approach, Yang Chen, Jianxue Wang, Siyuan Wang, Rui Bo, Chenjia Gu, Qingtao Li

Electrical and Computer Engineering Faculty Research & Creative Works

The integrated electricity and heat system (IEHS) is an emerging demand-side flexible resource for power systems. IEHS operators participating in electricity markets considering their capabilities in reserve provision will face the reserve deliverability risk due to the energy-limited storage nature of heat systems. To address this challenge and increase profitability, a distributionally robust joint chance-constrained mechanism with enhanced quantifications is adopted for the heating system and reserve deployment uncertainties. Detailed pipeline storage representation for thermal networks and integrated demand response are incorporated into this strategic participation model. A two-stage distributionally robust joint chance constrained program is then incorporated to effectively …

Continual Reinforcement Learning Formulation For Zero-Sum Game-Based Constrained Optimal Tracking, Behzad Farzanegan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This study provides a novel reinforcement learning-based optimal tracking control of partially uncertain nonlinear discrete-time (DT) systems with state constraints using zero-sum game (ZSG) formulation. To address optimal tracking, a novel augmented system consisting of tracking error and its integral value, along with an uncertain desired trajectory, is constructed. A barrier function (BF) with a tradeoff factor is incorporated into the cost function to keep the state trajectories to remain within a compact set and to balance safety with optimality. Next, by using the modified value functional, the ZSG formulation is introduced wherein an actor–critic neural network (NN) framework is …